Preimplantation Genetic Testing for Aneuploidy in <i>In Vitro</i> Fertilization Using Comprehensive Chromosome Screening: A Systematic Review and Meta-Analysis.
Bibliographic record
Abstract
fertilization (IVF) outcomes among randomized controlled trials (RCTs). We conducted a systematic search to identify RCTs comparing women undergoing PGT-A with CSS with women not undergoing PGT-A, from inception to December 2020. Random effects meta-analysis was utilized to calculate average odds ratios (OR) for clinical pregnancy rate (CPR), ongoing pregnancy rate (OPR), and miscarriage rate (MR). The heterogeneity of exposure was assessed using Forest plots and I2 statistics. Publication bias was evaluated using Egger's test. Among 1251 citations, seven RCTs met the inclusion criteria. Biopsies of embryos were carried out at various developmental stages, including polar body, day 3, and day 5-6 of culture. Data was analyzed as all studies and blastocyst only. Meta-analysis failed to show improvement in OPRs using PGT-A in the all ages, <35 years old and ≥35 years old age groups. There was also no significant difference in CPRs in any group. The MR decreased with the use of PGT-A (among all biopsy types and among blastocyst biopsies) in the all-ages group, but not when stratifying according to patient age <35 and ≥35 years old. More data regarding the risks and advantages of PGT-A are needed to make a final decision on the value of this intervention in clinical practice. The exact magnitude of the benefit of PGT-A selection cannot be correctly determined until multiple standardized protocol IVF PGT-A trials are conducted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.027 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".